The Future of Healthcare

Over one-third of all data in the world is human health data, and with that, AI and machine learning (ML) are changing what’s possible in terms of drug discovery.

Forty years ago, the era of the “small molecule” began, in which a single-molecule drug was developed on a standalone basis, using a one-size-fits-all approach. However, because people are unique, one size doesn’t fit all. Maybe only a quarter to a half of the patient population is responsive to a given drug. It’s like trying the same code on every lock you come across… It works sometimes, but not all the time, and it doesn’t account for the diversity of the challenge.

Regime Change

The era of small molecules and "popular health" (the playbook of the 1980s and 90s) is now ending, to be replaced by “precision medicine”. The linear orthodoxy of drug discovery has been a rigid flow-chart moving from target validation through to Phase III, focused on chemistry. However, this is becoming outdated. We have now entered a period where the ability to intervene in disease is unrecognisable from previous decades, with a greater focus on data and technology than on chemistry.

The new paradigm is: Measurement > Intervention.

• The Old World: Go from the lab, forward. Start with chemistry, and hope it fits the patient population.
• The New World: Go from the doctor’s clinic, backwards. Measure the disease at a much more granular scale, identify the specific patient, and then design the intervention.

This shift moves the industry away from linear workflows toward complex ecosystems of partnerships and collaborations. The 10- to 15-year product cycles, the three trial phases, the hundreds of millions in capex, the low success rates, the low patient-fit… it is all beginning to change.

The Data Advantage

Health data is the raw material for a higher probability of success, and it is why AI/Machine Learning have great potential in drug development. Where human analysis hits a ceiling, AI/ML takes over. It identifies signals amidst noise, spotting patterns the human brain cannot, partly because of the sheer scale and complexity of the data. This deep learning has the potential to transform drug development from a game of chance to a game of precision.

This matters because, despite the explosion in technological capability, the macro health signals remain bearish: ageing populations, rising obesity, and continued growth in chronic diseases. Technology has advanced, yet some general health metrics continue to deteriorate. By speeding up the process, reducing costs, increasing success rates, and serving more of the patient population, technology will be key to changing that narrative.

Our tenth ByteTree Quality stock has centuries of heritage, and yet it is at the vanguard of the future of pharma.